Humanoid Robots and Conversational Recommender Systems: a Preliminary Study

Conversational Recommender Systems (CoRSs) implement a paradigm in which users can interact with the system to define their preferences and discover items that best fit their needs. When the CoRS is implemented as a dialog agent, user and recommender interact by exchanging text messages. However, there is little evidence on how effective the interaction is when the CoRS is implemented through a Social Humanoid Robot. In this paper, we evaluate the possibility of introducing an interface based on a Social Humanoid Robot in ConveRSE, a domain-independent framework for the development of Conversational Recommender Systems. The novel interface will be compared against the existing chatbot-based one. The objective is to discover whether the framework can adapt to the new interface without worsening user experience and accuracy. We carried out a preliminary study, which involved 20 subjects. Results proved that, even though there are differences in how users approach the system using the two interfaces, there is no significant difference in its performance.